Collections of interconnected stories exploring themes in data science and AI
Five stories on production drift: silent scores, covariate shift, concept change, noise vs shift, and governance across modalities.
Eight chapters run on CPU simulators: physics-informed learning on an oscillator, combinatorial problems as bitstrings, variational circuits with Qiskit and PennyLane, a hybrid classifier, finance and routing QUBOs, tensor-train compression for language-model blocks, and a closing deployment narrative.
Five stories exploring quantum principles in reinforcement learning: superposition, entanglement, interference, tunnelling, and mixed states. Each story explains a quantum concept and shows how it improves RL algorithms, using theoretical examples and classic environments.
Six story-length answers to the questions people ask when they move from quantum buzzwords to running real circuits: shots, errors, correction, suppression, mitigation, transpilation, simulators, and algorithm parameters.